Interpreting Market Data Signals

A lack of supervisory familiarity with market data signals currently
hinders the incorporation of market data into summary assessments of bank
condition. In short, supervisors are unsure what constitutes a significant
level, change or trend in a market data signal like an SND spread or a
failure probability derived from an equity-based model. Information on
the distribution of past values of such market data signals can help address
this supervisory concern. Such analyses are useful not because of their
predictive ability but because they can provide "benchmarks" to place
the market data signals supervisors observe into a recent historical context.

An analysis of the past levels of SND spreads for large banking
organizations can provide useful information about what constitutes
an "atypical" SND spread in the current period. Graph 1 shows this
market data signal at the 75th percentile (the SND spread that is
greater than 75 percent of all observations) and the 25th percentile
(the SND spread that is greater than only 25 percent of all observations)
based on data from 145 SND issues from approximately 50 banking
organizations that had outstanding SND during the last six years.
Supervisors can use such information to determine if SND spreads
at institutions they supervise fall between levels they consider
"normal" based on recent history. For example, if supervisors observed
an SND spread at a bank of 250 basis points (a basis point is 1/100
of a percentage point), they could reasonably conclude that the
market's perception of risk at the bank was abnormally high, given
that SND spreads over the past two years have ranged between 80
and 200 basis points for most banks issuing traded SND.

Graph 2 displays the results of a similar analysis performed on
the failure probabilities for roughly 600 commercial banks that
were generated by one firm's equity-based model.*
As with SND spreads, supervisors could use this information
to determine if the observed failure probability were "atypical"
based on recent historical values. If the bank in the previous example
had a failure probability of 2.5 percent, supervisors again could
reasonably conclude that the market considered its level of risk
atypically high relative to the performance of the banking industry
during the last two years.

In addition to knowing if the levels of a market data signal are
"normal," supervisors also are concerned about whether changes or
movements in the signal are abnormally large. The datasets used
in the above analyses were re-examined to determine what constituted
a typical change in the signal. For example, only 15 percent of
the monthly changes in SND spreads for commercial banks were greater
than plus/minus 20 basis points. Likewise, only 15 percent of the
monthly changes in the failure probabilities were greater than plus/minus
30 percent.

As noted earlier, these analyses provide context for the market
data signals that supervisors observe. The period for which we reviewed
data was a particularly prosperous time for banks. Comparisons between
future values and these "benchmarks" hold only to the degree current
and future periods are similar.

*The model used for this
analysis is KMV LLC's Credit Monitor(r) and the failure probabilities
that it calculates are termed "expected default frequencies," or
EDFs(tm).